Summary: Background: Clinical decision-making is increasingly shifting towards data-driven approaches and requires large databases to develop state-of-the-art algorithms for diagnosing, detecting and predicting diseases.The intensive care unit (ICU), a data-rich setting, faces challenges with high-frequency, unstructured monitor data.Here, we showc
Patients with single right ventricle and tricuspid regurgitation have increased morbidity and mortality rates.The outcomes of surgical tricuspid valve repair and replacement are dismal.Although some centers prefer valve replacement with a bioprosthetic valve in the tricuspid position for the pediatric population, these valves have limited longevity
Abstract Considering global developments like climate change and the depletion of fossil resources, the use of new and sustainable feedstocks such here as lignocellulosic biomass becomes inevitable.Green waste comprises heterogeneous lignocellulosic biomass with low lignin content, which does not stem from agricultural processes or purposeful culti
The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns.We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange).ASD
Multiple endocrine neoplasia type 1 (MEN1) is an autosomal-dominant disorder characterized by tumors of the pituitary, parathyroid, and endocrine-gastrointestinal tract.Pituitary neuroendocrine tumors (PitNETs) occur in about 40% of MEN1 cases, with 10% being the first manifestation.Recent studies show a slight female predominance, with microPitNET